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Attempts to allocate capital across a selection of different investments areoften hampered by the fact that investors' decisions are made under limitedinformation (no historical return data) and during an extremely limitedtimeframe. Nevertheless, in some cases, rational investors with a certain levelof experience are able to ordinally rank investment alternatives throughrelative assessments of the probabilities that investments will be successful.However, to apply traditional portfolio optimization models, analysts must usehistorical (or simulated/expected) return data as the basis for theircalculations. This paper develops an alternative portfolio optimizationframework that is able to handle this kind of information (given by an ordinalranking of investment alternatives) and to calculate an optimal capitalallocation based on a Cobb-Douglas function, which we call the Sorted WeightedPortfolio (SWP). Considering risk-neutral investors, we show that the resultsof this portfolio optimization model usually outperform the output generated bythe (intuitive) Equally Weighted Portfolio (EWP) of different investmentalternatives, which is the result of optimization when one is unable toincorporate additional data (the ordinal ranking of the alternatives). Tofurther extend this work, we show that our model can also address risk-averseinvestors to capture correlation effects.

On Capital Allocation under Information Constraints